





'''simple docstring'''


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a__			:					List[str]       						=						200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
a__			:					Tuple       						=						50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
a__			:					Any       						=						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			str )							->     tuple[str, float]:

       """simple docstring"""


       UpperCAmelCase  =						len([g for position, g in enumerate(SCREAMING_SNAKE_CASE_ ) if g == main_target[position]] )
       return (item, float(SCREAMING_SNAKE_CASE_ ))
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			str )							->     tuple[str, str]:

       """simple docstring"""


       UpperCAmelCase  =						random.randint(0			,	len(SCREAMING_SNAKE_CASE_ ) - 1 )
       UpperCAmelCase  =						parent_a[:random_slice] + parent_a[random_slice:]
       UpperCAmelCase  =						parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			list[str] )							->     str:

       """simple docstring"""


       UpperCAmelCase  =						list(SCREAMING_SNAKE_CASE_ )
       if random.uniform(0			,	1 ) < MUTATION_PROBABILITY:
              UpperCAmelCase  =						random.choice(SCREAMING_SNAKE_CASE_ )
       return "".join(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			tuple[str, float]			,	SCREAMING_SNAKE_CASE_ :			list[tuple[str, float]]			,	SCREAMING_SNAKE_CASE_ :			list[str]			,	)							->     list[str]:

       """simple docstring"""


       UpperCAmelCase  =						[]
       # Generate more children proportionally to the fitness score.
       UpperCAmelCase  =						int(parent_a[1] * 100 ) + 1
       UpperCAmelCase  =						10 if child_n >= 10 else child_n
       for _ in range(SCREAMING_SNAKE_CASE_ ):
              UpperCAmelCase  =						population_score[random.randint(0			,	SCREAMING_SNAKE_CASE_ )][0]

              UpperCAmelCase,					UpperCAmelCase  =						crossover(parent_a[0]			,	SCREAMING_SNAKE_CASE_ )
              # Append new string to the population list.
              pop.append(mutate(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ) )
              pop.append(mutate(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ) )
       return pop
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			list[str]			,	SCREAMING_SNAKE_CASE_ :			bool = True )							->     tuple[int, int, str]:

       """simple docstring"""


       if N_POPULATION < N_SELECTED:
              UpperCAmelCase  =						f"{N_POPULATION} must be bigger than {N_SELECTED}"
              raise ValueError(SCREAMING_SNAKE_CASE_ )
       # Verify that the target contains no genes besides the ones inside genes variable.
       UpperCAmelCase  =						sorted({c for c in target if c not in genes} )
       if not_in_genes_list:
              UpperCAmelCase  =						f"{not_in_genes_list} is not in genes list, evolution cannot converge"
              raise ValueError(SCREAMING_SNAKE_CASE_ )

       # Generate random starting population.
       UpperCAmelCase  =						[]
       for _ in range(SCREAMING_SNAKE_CASE_ ):
              population.append(''''''.join([random.choice(SCREAMING_SNAKE_CASE_ ) for i in range(len(SCREAMING_SNAKE_CASE_ ) )] ) )

       # Just some logs to know what the algorithms is doing.
       UpperCAmelCase,					UpperCAmelCase  =						0, 0

       # This loop will end when we find a perfect match for our target.
       while True:
              generation += 1
              total_population += len(SCREAMING_SNAKE_CASE_ )

              # Random population created. Now it's time to evaluate.

              # Adding a bit of concurrency can make everything faster,
              #
              # import concurrent.futures
              # population_score: list[tuple[str, float]] = []
              # with concurrent.futures.ThreadPoolExecutor(
              #                                   max_workers=NUM_WORKERS) as executor:
              #     futures = {executor.submit(evaluate, item) for item in population}
              #     concurrent.futures.wait(futures)
              #     population_score = [item.result() for item in futures]
              #
              # but with a simple algorithm like this, it will probably be slower.
              # We just need to call evaluate for every item inside the population.
              UpperCAmelCase  =						[evaluate(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ) for item in population]

              # Check if there is a matching evolution.
              UpperCAmelCase  =						sorted(SCREAMING_SNAKE_CASE_			,	key=lambda SCREAMING_SNAKE_CASE_ : x[1]			,	reverse=SCREAMING_SNAKE_CASE_ )
              if population_score[0][0] == target:
                     return (generation, total_population, population_score[0][0])

              # Print the best result every 10 generation.
              # Just to know that the algorithm is working.
              if debug and generation % 10 == 0:
                     print(
                         f"\nGeneration: {generation}"
                         f"\nTotal Population:{total_population}"
                         f"\nBest score: {population_score[0][1]}"
                         f"\nBest string: {population_score[0][0]}" )

              # Flush the old population, keeping some of the best evolutions.
              # Keeping this avoid regression of evolution.
              UpperCAmelCase  =						population[: int(N_POPULATION / 3 )]
              population.clear()
              population.extend(SCREAMING_SNAKE_CASE_ )
              # Normalize population score to be between 0 and 1.
              UpperCAmelCase  =						[
                  (item, score / len(SCREAMING_SNAKE_CASE_ )) for item, score in population_score
              ]

              # This is selection
              for i in range(SCREAMING_SNAKE_CASE_ ):
                     population.extend(select(population_score[int(SCREAMING_SNAKE_CASE_ )]			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ) )
                     # Check if the population has already reached the maximum value and if so,
                     # break the cycle.  If this check is disabled, the algorithm will take
                     # forever to compute large strings, but will also calculate small strings in
                     # a far fewer generations.
                     if len(SCREAMING_SNAKE_CASE_ ) > N_POPULATION:
                            break


if __name__ == "__main__":
    a__			:					int       						=						(
        'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
    )
    a__			:					Dict       						=						list(
        ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
        'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
    )
    a__						,		a__						,		a__			:					Optional[int]       						=						basic(target_str, genes_list)
    print(
        F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
    )




from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)

lowercase    :   Union[str, Any]						=			{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'efficientformer'







	def __init__(   self						:Union[str, Any]	,  a						:List[int] = [3, 2, 6, 4]	,  a						:List[int] = [4_8, 9_6, 2_2_4, 4_4_8]	,  a						:List[bool] = [True, True, True, True]	,  a						:int = 4_4_8	,  a						:int = 3_2	,  a						:int = 4	,  a						:int = 7	,  a						:int = 5	,  a						:int = 8	,  a						:int = 4	,  a						:float = 0.0	,  a						:int = 1_6	,  a						:int = 3	,  a						:int = 3	,  a						:int = 3	,  a						:int = 2	,  a						:int = 1	,  a						:float = 0.0	,  a						:int = 1	,  a						:bool = True	,  a						:bool = True	,  a						:float = 1E-5	,  a						:str = "gelu"	,  a						:float = 0.02	,  a						:float = 1E-1_2	,  a						:int = 2_2_4	,  a						:float = 1E-0_5	,  **a						:str	,  )						->					None:
								super().__init__(**a		)

								__UpperCamelCase      :  Optional[Any]       =   hidden_act
								__UpperCamelCase      :  Any       =   hidden_dropout_prob
								__UpperCamelCase      :  str       =   hidden_sizes
								__UpperCamelCase      :  Dict       =   num_hidden_layers
								__UpperCamelCase      :  Optional[Any]       =   num_attention_heads
								__UpperCamelCase      :  str       =   initializer_range
								__UpperCamelCase      :  List[str]       =   layer_norm_eps
								__UpperCamelCase      :  str       =   patch_size
								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  Any       =   depths
								__UpperCamelCase      :  Tuple       =   mlp_expansion_ratio
								__UpperCamelCase      :  List[str]       =   downsamples
								__UpperCamelCase      :  Optional[int]       =   dim
								__UpperCamelCase      :  Dict       =   key_dim
								__UpperCamelCase      :  Optional[Any]       =   attention_ratio
								__UpperCamelCase      :  Dict       =   resolution
								__UpperCamelCase      :  Union[str, Any]       =   pool_size
								__UpperCamelCase      :  Tuple       =   downsample_patch_size
								__UpperCamelCase      :  Optional[int]       =   downsample_stride
								__UpperCamelCase      :  Optional[int]       =   downsample_pad
								__UpperCamelCase      :  Union[str, Any]       =   drop_path_rate
								__UpperCamelCase      :  Union[str, Any]       =   num_metaad_blocks
								__UpperCamelCase      :  List[str]       =   distillation
								__UpperCamelCase      :  str       =   use_layer_scale
								__UpperCamelCase      :  Tuple       =   layer_scale_init_value
								__UpperCamelCase      :  str       =   image_size
								__UpperCamelCase      :  int       =   batch_norm_eps